Audio data recovery method, device and Bluetooth device

ABSTRACT

An audio data recovery method, an audio data recovery device and a Bluetooth device are described. The audio data recovery method comprises: dividing audio data into a first frequency domain component and a second frequency domain component in a frequency domain; using a second data recovery algorithm to recover the audio data in the second frequency domain component; and using a first data recovery algorithm with lower complexity than the second data recovery algorithm to recover the audio data in the first frequency domain component.

CROSS-REFERENCES TO RELATED APPLICATIONS

This patent application is a continuation of PCT/CN2019/128776 filed onDec. 26, 2019, which claims the priority of Chinese Patent ApplicationNo.: 201811621631.X, filed on Dec. 28, 2018 in China, and the entirecontent of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure relates to the field of Bluetooth technology, andin particular to an audio data recovery method, an audio data recoverydevice, and a Bluetooth device.

Description of the Related Art

The transmission bandwidth is limited in Bluetooth channel. Atransmitter (a.k.a., master) of Bluetooth audio transmission needs tocompress audio signal before transmission, and then sends the compressedaudio signal to a receiver (a.k.a., slave) through wirelesstransmission. When a distance between the master device and the salvedevice is beyond a certain distance, or even the distance between themaster device and the salve device is close, there is interference,packet loss or packet error may occur in the wireless transmission. As aresult, the audio signal received by the slave device appears jam andnoisy during decoding and playback.

There are at least two common approaches to solve the audio data errorcaused by packet loss or packet error. One is to add redundantinformation, such as CRC check, error correction code, and otherimportant coding information protection mechanisms, to the code streamwhen the master device is transmitting. The other is to use correlationof the audio signal itself, and use good data packets before and afterthe lost or error data packet to recover the lost or error data packeton the slave device. A simplest method is to insert a silent frame dataor repeat a previous good frame data. A more complex method is amodel-based interpolation. For example, an interpolation based onautoregressive AR model or sine model is used to recover the audio data.However, these two methods have poor recovery quality, especially in thecase of higher packet loss rates. Therefore, there is a need for animproved technical solution to the above-mentioned problems.

SUMMARY OF THE INVENTION

An audio data recovery method, an audio data recovery device and aBluetooth device are provided according to embodiments of the presentinvention to solve the above technical problems.

According to one aspect of the present invention, an audio data recoverymethod comprises: dividing audio data into a first frequency domaincomponent and a second frequency domain component in a frequency domain;using a second data recovery algorithm to recover the audio data in thesecond frequency domain component; and using a first data recoveryalgorithm with lower complexity than the second data recovery algorithmto recover the audio data in the first frequency domain component.

According to another aspect of the present invention, an audio datarecovery device is provided and comprises: a classification moduleconfigured for dividing audio data into a first frequency domaincomponent and a second frequency domain component in a frequency domain;a first recovery module configured for using a second data recoveryalgorithm to recover the audio data in the second frequency domaincomponent; and a second recovery module configured for using a firstdata recovery algorithm with lower complexity than the second datarecovery algorithm to recover the audio data in the first frequencydomain component.

According to another aspect of the present invention, a Bluetooth deviceis provided and comprises: an audio data recovery device. The audio datarecovery device comprises: a classification module configured fordividing audio data into a first frequency domain component and a secondfrequency domain component in a frequency domain; a first recoverymodule configured for using a second data recovery algorithm to recoverthe audio data in the second frequency domain component; and a secondrecovery module configured for using a first data recovery algorithmwith lower complexity than the second data recovery algorithm to recoverthe audio data in the first frequency domain component.

One of the advantages, features or advantages of the present inventionis that the audio data is divided into a first frequency domaincomponent and a second frequency domain component in a frequency domain,a second data recovery algorithm is used to recover the audio data inthe second frequency domain component; and a first data recoveryalgorithm with lower complexity than the second data recovery algorithmis used to recover the audio data in the first frequency domaincomponent, so that computational complexity of audio data recovery isreduced, and high-quality audio recovery can be achieved on theBluetooth devices without sufficient computational resources.

There are many other objects, together with the foregoing attained inthe exercise of the invention in the following description and resultingin the embodiment illustrated in the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood with regard to the followingdescription, appended claims, and accompanying drawings wherein:

FIG. 1 is a schematic flow chart showing an audio data recovery methodaccording to one embodiment of the present invention;

FIG. 2 is a schematic structural diagram showing an audio data recoverydevice according to one embodiment of the present invention;

FIG. 3 is a schematic structural diagram showing a Bluetooth deviceaccording to one embodiment of the present invention; and

FIG. 4 is a schematic flow chart showing a Bluetooth audio processaccording to one embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The detailed description of the invention is presented largely in termsof procedures, operations, logic blocks, processing, and other symbolicrepresentations that directly or indirectly resemble the operations ofdata processing devices that may or may not be coupled to networks.These process descriptions and representations are typically used bythose skilled in the art to most effectively convey the substance oftheir work to others skilled in the art.

Reference herein to “one embodiment” or “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiment can be comprised in at least one embodiment of theinvention. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment, nor are separate or alternative embodiments mutuallyexclusive of other embodiments. Further, the order of blocks in processflowcharts or diagrams representing one or more embodiments of theinvention do not inherently indicate any particular order nor imply anylimitations in the invention.

Gapped-data Amplitude and Phase Estimation (GAPES) algorithm is wellknown and commonly used to recover audio data in the prior art. Comparedwith other commonly used approaches, various methods based on the GAPESalgorithm have significantly improved quality of the recovered audiodata, and demonstrate good effects even at a 30% packet loss rate.However, the computational complexity involving with the GAPES algorithmis of high, and not practical for some Bluetooth audio devices withoutsufficient computational resources.

The detailed introduction of the GAPES algorithm can be found in thepaper “Packet Loss Concealment for Audio Streaming Based on the GAPESAlgorithm” published by Hadas Ofir and David Malah in 2005, which ishereby incorporated by reference. One of the objectives, advantages andbenefits in the present invention is of low-complexity based on theGAPES algorithm. Various embodiments of the present inventions haveproven practical for Bluetooth audio devices without sufficientcomputational resources, to improve the quality of the recovered audiodata.

FIG. 1 is a schematic flowchart showing an audio data recovery methodaccording to one embodiment of the present invention. As shown in FIG.1, the audio data recovery method comprises: dividing audio data into afirst frequency domain component and a second frequency domain componentin a frequency domain at 101; using a second data recovery algorithm torecover the audio data in the second frequency domain component at 102;and using a first data recovery algorithm with lower complexity than thesecond data recovery algorithm to recover the audio data in the firstfrequency domain component at 103.

After the audio data is received, an audio data receiver can perform atime-frequency transform on the audio data to transform a time domainsignal into a frequency domain signal, then divide the audio data intotwo types of frequency domain components (namely, the first frequencycomponent and the second frequency-domain component) in the frequencydomain, and use different audio data recovery algorithms for the twotypes of frequency domain components to recover the audio data.

The audio data is divided into two types of frequency domain components.One type is the first frequency component, and the other type is thesecond frequency-domain component. The second data recovery algorithm isused to recover the audio data in the second frequency domain component.The first data recovery algorithm with lower complexity than the seconddata recovery algorithm is used to recover the audio data in the firstfrequency domain component. In this way, only a few of frequency domaincomponents need to be estimated by the second data recovery algorithm,which can greatly reduce the computational complexity, and it canachieve high-quality audio recovery even on the Bluetooth deviceswithout sufficient computational resources.

In one embodiment, the first frequency domain component is a noise-likecomponent, and the second frequency domain component is a tone-dominantcomponent. A power of the tone-dominant component is higher than a powerof the noise-like component. The second data recovery algorithm is aGapped-data Amplitude and Phase Estimation algorithm. The first datarecovery algorithm is a noise shaping and random phase algorithm.

In one embodiment, it is assumed a time-frequency transform (e.g.,Fourier Transform or FFT) has been applied to the audio data that is nowin frequency domain. The (transformed) audio data is divided into afirst frequency domain component and a second frequency domain componentin a frequency domain at 101. In one embodiment, the audio data isdivided into a first type of frame data and a second type of frame datain the frequency domain; and dividing the first type of frame data intothe first frequency domain component and the second frequency domaincomponent.

The first type of frame data may be incorrect audio data, which isusually caused by packet loss or error packet. It can also be called asproblem frame, incorrect frame, or bad frame, etc. The second type offrame data may be correct audio data, or be called as good frame, goodaudio data or good packet, etc. After the audio data is divided into thecorrect audio data and the incorrect audio data, only the incorrectaudio data can be divided into the frequency domain components, and thenthe divided frequency domain components can be recovered separately.

In one embodiment, whether the current frame audio data is the firsttype of frame data or the second type of frame data is determinedaccording to a check mechanism or an error frame flag of the audio data,which will not be further described herein to avoid obscuring aspects ofthe present invention. Those skilled in the art know there are variousmethods to indicate whether a frame of audio data is perfect or includeserrors.

In one embodiment, dividing the first type of frame data into the firstfrequency domain component and the second frequency domain componentcomprises: estimating a power spectrum of the first type of frame data;determining peaks of the power spectrum; determining candidate frequencydomain components according to the peaks; and regarding the candidatefrequency domain components whose power is greater than a preset orpredefined threshold as the second frequency domain component, and othercandidate frequency domain components as the first frequency domaincomponent.

In one embodiment, a local maximum method can be used to find the peaksof the estimated power spectrum. In one embodiment, estimating a powerspectrum of the first type of frame data comprises: calculating thepower spectrum of the first type of frame data according to followingformula:

P _(m)(k)=|X _(m) ₁ (k)|² +|X _(m) ₂ (k)|²,

where m is a sequence number of the current first type of frame, m₁ is asequence number of the previous second type of frame adjacent to thecurrent first type of frame, m₂ is a sequence number of the next secondtype of frame data adjacent to the current first type of frame, |X_(m) ₁(k)| is spectrum data of a m₁th frame, and |X_(m2)(k)| is spectrum dataof a m₂th frame. Specifically, suppose that X_(m)(k) is the spectrumdata of FFT bins of the mth frame, k is the number of the FFT bin, 1≤k≤Land L is the length of the FFT transform.

The spectrum data of the previous second of type of frame adjacent tothe current frame (the first type of frame) and the spectrum data of thenext second type of frame adjacent to the current frame can be used toestimate the power spectrum of the current frame.

In one embodiment, the determining candidate frequency domain componentsaccording to the peaks comprises: sorting the peaks from large to small;and determining the frequency domain components with the first N peaksafter sorting as centers and within a preset length as the candidatefrequency domain components. N can be a positive integer, for example, Ncan be 10. For each peak PeakBin picked out, a window with the peakPeakBin as a center can be added, and the FFT bin within the window canbe regarded as one candidate frequency domain component WinBin of thetone bin (tone-dominant component). The length of this window can be setto a value equal to 3 or 5.

In one embodiment, the using a first data recovery algorithm with lowercomplexity than the second data recovery algorithm to recover the audiodata in the first frequency domain component comprises: recovering theaudio data in the first frequency domain component according tofollowing formula:

{circumflex over (X)} _(m)(k)=s(k)α(k)X _(m−1)(k),   i.

wherein, s(k) is a random variable, a value of s(k) is {1, −1}; α(k) isan amplitude shaping factor; m is a sequence number of the currentframe(the first type of frame); m−1 is a sequence number of the previousframe adjacent to the current frame; |X_(m−1)(k)| is spectrum data of am−1 th frame.

The current frame is recovered by using the random variable s(k) to adda random phase to the spectrum data of the previous frame and combiningwith the amplitude shaping factor α(k) to the spectrum data of theprevious frame.

In one embodiment, the amplitude shaping factor can be a presetconstant. For example, the amplitude shaping factor is 0.9. In anotherembodiment, the amplitude shaping factor is calculated according tofollowing formula:

${\alpha^{2}(k)} = \frac{\sum\limits_{k \in B_{b}}\;{{X_{m_{1}}(k)}}^{2}}{\sum\limits_{k \in B_{b}}\;{{X_{m_{2}}(k)}}^{2}}$

wherein, B_(b) is a critical subband of the spectrum; m₁ is a sequencenumber of the previous second type of frame adjacent to the currentframe, m₂ is a sequence number of the next second type of frame dataadjacent to the current frame, |X_(m) ₁ (k)| is spectrum data of a m₁thframe, and |X_(m2)(k)| is spectrum data of a m₂th frame.

In one embodiment, the entire spectrum can be divided into a pluralityof subbands (bin). One corresponding amplitude shaping factor can becalculated for each subband.

Based on the same inventive concept, an audio data recovery device isalso provided according to one embodiment of the present invention.Since the audio data recovery device solves the same problem withsimilar principle with the audio data recovery method provided in thefirst embodiment of the present invention, the implementation of theaudio data recovery device can refer to the implementation of themethod, and the repetition will not be repeated.

FIG. 2 is a schematic structural diagram of an audio data recoverydevice according to one embodiment of the present invention. As shown inFIG. 2, the audio data recovery device includes: a classification module201 configured for dividing audio data into a first frequency domaincomponent and a second frequency domain component in a frequency domain;a first recovery module 202 configured for using a second data recoveryalgorithm to recover the audio data in the second frequency domaincomponent; and a second recovery module 203 configured for using a firstdata recovery algorithm with lower complexity than the second datarecovery algorithm to recover the audio data in the first frequencydomain component.

In the audio data recovery device of the present invention, the audiodata is divided into two types of frequency domain components. One typeis the first frequency component, and the other type is the secondfrequency-domain component. The second data recovery algorithm is usedto recover the audio data in the second frequency domain component. Thefirst data recovery algorithm with lower complexity than the second datarecovery algorithm is used to recover the audio data in the firstfrequency domain component. In this way, only a few of frequency domaincomponents need to be estimated by the second data recovery algorithm,which can greatly reduce the computational complexity, and it canachieve high-quality audio recovery even on the Bluetooth deviceswithout sufficient computational resources.

In one embodiment, the classification module comprises: a firstclassification unit configured for dividing the audio data into a firsttype of frame data and a second type of frame data in the frequencydomain; and a second classification unit configured for dividing thefirst type of frame data the first frequency domain component and thesecond frequency domain component.

In one embodiment, the first type of frame data is incorrect data, andthe second type of frame data is correct data. In one embodiment, thesecond classification unit comprises: an estimation subunit configuredfor estimating a power spectrum of the first type of frame data; a peakvalue determination subunit configured for determining peaks of thepower spectrum; a candidate frequency domain component determiningsubunit configured for determining candidate frequency domain componentsaccording to the peaks; and a classification subunit configured forregarding the candidate frequency domain components whose power isgreater than a preset threshold as the second frequency domaincomponent, and other candidate frequency domain components as the firstfrequency domain component.

In one embodiment, the estimation subunit is configured for calculatingthe power spectrum of the first type of frame data according tofollowing formula:

P _(m)(k)=|X _(m) ₁ (k)|² +|X _(m) ₂ (k)|²,

where m is a sequence number of the current first type of frame, m₁ is asequence number of the previous second type of frame adjacent to thecurrent first type of frame (current frame), m₂ is a sequence number ofthe next second type of frame data adjacent to the current first type offrame, |X_(m) ₁ (k)| is spectrum data of a m₁th frame, and |X_(m2)(k)|is spectrum data of a m₂th frame.

In one embodiment, a local maximum method can be used to find the peaksof the estimated power spectrum. In one embodiment, the candidatefrequency domain component determining subunit is configured for:sorting the peaks from large to small; and determining the frequencydomain components with the first n peaks after sorting as centers andwithin a preset length as the candidate frequency domain components.

In one embodiment, the second recovery module is configured for:recovering the audio data in the first frequency domain componentaccording to following formula:

{circumflex over (X)} _(m)(k)=s(k)α(k)X _(m−1)(k),   ii.

wherein, s(k) is a random variable, a value of s(k) is {1, −1}; α(k) isan amplitude shaping factor; m is a sequence number of the current firsttype of frame; m−1 is a sequence number of the previous frame adjacentto the current first type of frame; |X_(m−1)(k)| is spectrum data of am−1th frame

In one embodiment, the amplitude shaping factor can be a presetconstant. In another embodiment, the amplitude shaping factor iscalculated according to following formula:

${\alpha^{2}(k)} = \frac{\sum\limits_{k \in B_{b}}\;{{X_{m_{1}}(k)}}^{2}}{\sum\limits_{k \in B_{b}}\;{{X_{m_{2}}(k)}}^{2}}$

wherein, B_(b) is a critical subband of the spectrum; m₁ is a sequencenumber of the previous second type of frame adjacent to the currentframe, m₂ is a sequence number of the next second type of frame dataadjacent to the current frame, |X_(m) ₁ (k)| is spectrum data of a m₁thframe, and |X_(m2)(k)| is spectrum data of a m₂th frame.

Based on the same inventive concept, a Bluetooth device is providedaccording to one embodiment of the present invention. Since theBluetooth device solves the same problem with similar principle with themethod provided in the first embodiment of the present invention, theimplementation of the Bluetooth device can refer to the method can referto the implementation of the method, and the repetition will not berepeated.

FIG. 3 is a schematic structural diagram of a Bluetooth device accordingto one embodiment of the present invention. As shown in FIG. 3, theBluetooth device includes: an audio data recovery device shown in FIG.2.

The Bluetooth device may be a Bluetooth headset, a Bluetooth speaker, aBluetooth gateway, a Bluetooth MP3, a Bluetooth flash disk, a Bluetoothvehicle-mounted device, a Bluetooth adapter, etc., which are not limitedin the present disclosure.

In the Bluetooth device of the present invention, the audio data isdivided into two types of frequency domain components. One type is thefirst frequency component, and the other type is the secondfrequency-domain component. The second data recovery algorithm is usedto recover the audio data in the second frequency domain component. Thefirst data recovery algorithm with lower complexity than the second datarecovery algorithm is used to recover the audio data in the firstfrequency domain component. In this way, only a few of frequency domaincomponents need to be estimated by the second data recovery algorithm,which can greatly reduce the computational complexity, and it canachieve high-quality audio recovery even on the Bluetooth deviceswithout sufficient computational resources.

FIG. 4 shows a schematic diagram showing a Bluetooth audio processaccording to one embodiment of the present invention. As shown in FIG.4, the Bluetooth audio process comprises following operations.

A time-frequency transform is performed on the received audio signal at401. Usually, Fast Fourier transform (FFT) is used to perform thetime-frequency transform. Before the FFT transform, an analysis windowcan be added to the time-domain audio signal, and then the FFT transformis performed. Since the FFT technology is a common technology in anexisting digital signal processing, it will not be repeated here.

The audio data comprises a plurality of frames and is processed inframe. Whether the current frame is an incorrect frame is determined at402. In one embodiment, data frames in the present invention are dividedinto correct frames and incorrect frames (for example, lost or errorframes, etc.).

If the current frame is a good frame, the spectrum data of the currentframe is buffered, and the process flow goes to 406. If the currentframe is a bad frame, the process flow goes to 403. A frequency domaincomponent classification is performed on the current frame at 403.

The power spectrum of the current frame is estimated according to thefrequency spectrum of the buffered good frame data. Each FFT bin isclassified by a peak detection method based on the estimated powerspectrum of the current frame.

If the FFT bin of the current incorrect frame is classified as thetone-dominant component, the process flow goes to 404. If the FFT bin ofthe current bad frame is classified as the noise-like component, theprocess flow goes to 405.

The GAPES algorithm is used to estimate the spectrum data of the FFT binat 404. The noise shaping and random phase algorithm is used to recoverthe spectrum data of the FFT bin at 405.

An inverse time-frequency transform is performed on the audio data inthe frequency domain at 406. Inverse fast Fourier transform IFFT is usedto perform the inverse time-frequency transform. A synthesis window isadded to the audio signal after the IFFT transform, and then overlap-addprocessing (overlap-add) is performed to obtain the reconstructed timedomain audio signal.

The following experiment is used to prove beneficial effects of thepresent invention.

Under normal circumstances, a 24-bit fixed-point digital signalprocessor (DSP) is used, and it need 12 MHz to estimate one FFT bin byusing the GAPES algorithm. Assuming 1024-point FFT is used, 513 FFT binsneed to be estimated in order to recover the current frame data.

If all the FFT bins are estimated by the GAPES algorithm, 12*513=6156MHz is required. If the method provided in the present invention isused, only a few of FFT bins are required to use the GAPES algorithm,and the other bins are recovered according to the noise shaping andrandom phase algorithm. It can be determined through the experiment thatthe audio quality obtained by estimating only 30 FFT bins based on theGAPES algorithm in the present invention is basically the same as theaudio quality obtained by estimating all the 513 FFT bins based on theGAPES algorithm in the related art at a packet error rate of 10%.

According to one aspect of the present invention, the present inventioncan be implemented as a nonvolatile computer-readable medium. Thenonvolatile computer-readable medium comprises instructions executed bya processor. The instructions cause the processor to perform: capturingambient sound; detecting the ambient sound, and triggering the headphoneto enter an interactive mode when a preset interested sound appears inthe ambient sound; controlling the headphone to output an interactivereminder in the interactive mode. The interactive reminder comprises onetype or a combination of multiple types of a visual reminder, a tactilereminder and an auditory reminder.

Those skilled in the art should be aware that the embodiments of thisapplication may be methods, systems, or computer program products.Accordingly, the present invention may take the form of a completehardware embodiment, a complete software embodiment, or an embodiment inconjunction with software and hardware aspects. Furthermore, the presentinvention may take the form of a computer program product implemented onone or more computer-available storage media (including, but not limitedto, disk memory, CD-ROM, optical memory, etc.) containingcomputer-available program code.

The present invention is described with reference to methods, equipment(systems), and flow charts and/or block diagrams of computer programproducts according to the embodiment of the present invention. It shouldbe understood that each flow and/or block in a flowchart and/or blockdiagram, as well as the combination of flow and/or block in a flowchartand/or block diagram, can be implemented by computer programinstructions. These computer program instructions may be provided to aprocessor of a general purpose computer, a dedicated computer, anembedded processor, or other programmable data processing device toproduce a machine such that instructions executed by a processor of acomputer or other programmable data processing device produceinstructions for implementing a flow chart or more. A device forprocesses and/or block diagrams or functions specified in a box ormultiple boxes.

These computer program instructions may also be stored in acomputer-readable memory that may guide a computer or other programmabledata processing device to work in a particular way, such that theinstructions stored in the computer-readable memory generate amanufacturer including an instruction device that is implemented in aflow chart one or more processes. Process and/or block diagram, a box orfunction specified in multiple boxes.

These computer program instructions may also be loaded on a computer orother programmable data processing device such that a series ofoperational steps are performed on a computer or other programmabledevice to produce computer-implemented processing, thereby providinginstructions executed on a computer or other programmable device forimplementing a flow chart. The steps of a process or multiple processesand/or block diagrams, or functions specified in a box.

Although preferred embodiments of the present invention have beendescribed, additional changes and modifications to these embodiments maybe made once the basic creative concepts are known to those skilled inthe art. The appended claims are therefore intended to be interpreted toinclude preferred embodiments and all changes and modifications fallingwithin the scope of this application.

Obviously, a person skilled in the art may make various changes andvariations to the application without departing from the spirit andscope of the application. Thus, if these modifications and variations ofthis application fall within the scope of the claims and theirequivalent technologies, the application is also intended to includethese changes and variations.

We claim:
 1. An audio data recovery method, comprising: dividing audiodata into a first frequency domain component and a second frequencydomain component in a frequency domain; using a second data recoveryalgorithm to recover the audio data in the second frequency domaincomponent; and using a first data recovery algorithm with lesscomplexity than the second data recovery algorithm to recover the audiodata in the first frequency domain component.
 2. The audio data recoverymethod according to claim 1, wherein the first frequency domaincomponent is a noise-like component, the second frequency domaincomponent is a tone-dominant component, and a power of the tone-dominantcomponent is higher than a power of the noise-like component.
 3. Theaudio data recovery method according to claim 1, wherein the second datarecovery algorithm is a Gapped-data Amplitude and Phase Estimationalgorithm.
 4. The audio data recovery method according to claim 1,wherein the first data recovery algorithm is a noise shaping and randomphase algorithm.
 5. The audio data recovery method according to claim 1,wherein the dividing audio data into a first frequency domain componentand a second frequency domain component in a frequency domain comprises:dividing the audio data into a first type of frame data and a secondtype of frame data in the frequency domain; and dividing the first typeof frame data into the first frequency domain component and the secondfrequency domain component.
 6. The audio data recovery method accordingto claim 5, wherein the first type of frame data is incorrect data, andthe second type of frame data is correct data.
 7. The audio datarecovery method according to claim 5, wherein the dividing the firsttype of frame data into the first frequency domain component and thesecond frequency domain component comprises: estimating a power spectrumof the first type of frame data; determining peaks of the powerspectrum; determining candidate frequency domain components according tothe peaks; and regarding the candidate frequency domain components whosepower is greater than a preset threshold as the second frequency domaincomponent, and other candidate frequency domain components as the firstfrequency domain component.
 8. The audio data recovery method accordingto claim 7, wherein the estimating a power spectrum of the first type offrame data comprises: calculating the power spectrum of the first typeof frame data according to following formula:P _(m)(k)=|X _(m) ₁ (k)|² +|X _(m) ₂ (k)|², where m is a sequence numberof the current first type of frame, m₁ is a sequence number of theprevious second type of frame adjacent to the current first type offrame, m₂ is a sequence number of the next second type of frame dataadjacent to the current first type of frame, |X_(m) ₁ (k)| is spectrumdata of a m₁th frame, and |X_(m2)(k)| is spectrum data of a m₂th frame.9. The audio data recovery method according to claim 7, wherein thedetermining candidate frequency domain components according to the peakscomprises: sorting the peaks from large to small; and determining thefrequency domain components with the first N peaks after sorting ascenters and within a preset length as the candidate frequency domaincomponents.
 10. The audio data recovery method according to claim 5,wherein the using a first data recovery algorithm with lower complexitythan the second data recovery algorithm to recover the audio data in thefirst frequency domain component comprises: recovering the audio data inthe first frequency domain component according to following formula:{circumflex over (X)} _(m)(k)=s(k)α(k)X _(m−1)(k), wherein, s(k) is arandom variable, a value of s(k) is {1, −1}; α(k) is an amplitudeshaping factor; m is a sequence number of the current first type offrame; m−1 is a sequence number of the previous frame adjacent to thecurrent first type of frame; |X_(m−1)(k)| is spectrum data of a m−1thframe.
 11. The audio data recovery method according to claim 10, whereinthe amplitude shaping factor is calculated according to followingformula:${\alpha^{2}(k)} = \frac{\sum\limits_{k \in B_{b}}\;{{X_{m_{1}}(k)}}^{2}}{\sum\limits_{k \in B_{b}}\;{{X_{m_{2}}(k)}}^{2}}$wherein, B_(b) is a critical subband of the spectrum; m₁ is a sequencenumber of the previous second type of frame adjacent to the currentfirst type of frame, m₂ is a sequence number of the next second type offrame data adjacent to the current first type of frame, |X_(m) ₁ (k)| isspectrum data of a m₁th frame, and |X_(m2)(k)| is spectrum data of am₂th frame.
 12. An audio data recovery device, comprising: aclassification module configured for dividing audio data into a firstfrequency domain component and a second frequency domain component in afrequency domain; a first recovery module configured for using a seconddata recovery algorithm to recover the audio data in the secondfrequency domain component; and a second recovery module configured forusing a first data recovery algorithm with lower complexity than thesecond data recovery algorithm to recover the audio data in the firstfrequency domain component.
 13. The audio data recovery device accordingto claim 12, wherein the first frequency domain component is anoise-like component, the second frequency domain component is atone-dominant component, and a power of the tone-dominant component ishigher than a power of the noise-like component; and wherein the seconddata recovery algorithm is a Gapped-data Amplitude and Phase Estimationalgorithm, and the first data recovery algorithm is a noise shaping andrandom phase algorithm.
 14. The audio data recovery device according toclaim 12, wherein the classification module comprises: a firstclassification unit configured for dividing the audio data into a firsttype of frame data and a second type of frame data in the frequencydomain; and a second classification unit configured for dividing thefirst type of frame data into the first frequency domain component andthe second frequency domain component.
 15. The audio data recoverydevice according to claim 14, wherein the first type of frame data isincorrect data, and the second type of frame data is correct data. 16.The audio data recovery device according to claim 14, wherein the secondclassification unit comprises: an estimation subunit configured forestimating a power spectrum of the first type of frame data; a peakvalue determination subunit configured for determining peaks of thepower spectrum; a candidate frequency domain component determiningsubunit configured for determining candidate frequency domain componentsaccording to the peaks; and a classification subunit configured forregarding the candidate frequency domain components whose power isgreater than a preset threshold as the second frequency domaincomponent, and other candidate frequency domain components as the firstfrequency domain component.
 17. The audio data recovery device accordingto claim 16, wherein the estimation subunit is configured forcalculating the power spectrum of the first type of frame data accordingto following formula:P _(m)(k)=|X _(m) ₁ (k)|² +|X _(m) ₂ (k)|², where m is a sequence numberof the current first type of frame, m₁ is a sequence number of theprevious second type of frame adjacent to the current first type offrame, m₂ is a sequence number of the next second type of frame dataadjacent to the current first type of frame, |X_(m) ₁ (k)| is spectrumdata of a m₁th frame, and |X_(m2)(k)| is spectrum data of a m₂th frame.18. The audio data recovery device according to claim 16, wherein thecandidate frequency domain component determining subunit is configuredfor: sorting the peaks from large to small; and determining thefrequency domain components with the first N peaks after sorting ascenters and within a preset length as the candidate frequency domaincomponents.
 20. The audio data recovery device according to claim 16,wherein the second recovery module is configured for: recovering theaudio data in the first frequency domain component according tofollowing formula:{circumflex over (X)} _(m)(k)=s(k)α(k)X _(m−1)(k), wherein, s(k) is arandom variable, a value of s(k) is {1, −1}; α(k) is an amplitudeshaping factor; m is a sequence number of the current first type offrame; m−1 is a sequence number of the previous frame adjacent to thecurrent first type of frame; |X_(m−1)(k)| is spectrum data of a m−1thframe.
 21. The audio data recovery device according to claim 20, whereinthe amplitude shaping factor is calculated according to followingformula:${\alpha^{2}(k)} = \frac{\sum\limits_{k \in B_{b}}\;{{X_{m_{1}}(k)}}^{2}}{\sum\limits_{k \in B_{b}}\;{{X_{m_{2}}(k)}}^{2}}$wherein, B_(b) is a critical subband of the spectrum; m₁ is a sequencenumber of the previous second type of frame adjacent to the currentfirst type of frame, m₂ is a sequence number of the next second type offrame data adjacent to the current first type of frame, |X_(m) ₁ (k)| isspectrum data of a m₁th frame, and |X_(m2)(k)| is spectrum data of am₂th frame.
 22. A Bluetooth device, comprising: an audio data recoverydevice, comprising: a classification module configured for dividingaudio data into a first frequency domain component and a secondfrequency domain component in a frequency domain; a first recoverymodule configured for using a second data recovery algorithm to recoverthe audio data in the second frequency domain component; and a secondrecovery module configured for using a first data recovery algorithmwith lower complexity than the second data recovery algorithm to recoverthe audio data in the first frequency domain component.